Burgard et al. introduced active localization in their seminal work [1]}. They demonstrated that, rather than passively driving a robot around, picking actions that reduce the expected localization uncertainty results in a more efficient and robust solution. Using an entropy measure characterized as a mixture-of-Gaussians, they demonstrate that the framework of Markov localization [2]} can be extended to action selection. This was more a proof-of-concept, as the applicability of this method is confined to low-dimensional state-spaces, where entropy computation can be carried out efficiently. Since then, several other approaches [3]}, [4]}, [5]}, [6]}, [7]}, [8]} use a similar, information gain maximization cost for the task of active localization or SLAM.
